Behavioral Economics & AI Ethics
Persona-Conditioned Risk Behavior in Large Language Models: A Simulated Gambling Study with GPT-4.1
Executive Impact
This paper explores how large language models (LLMs), specifically GPT-4.1, exhibit human-like risk behaviors when assigned different socioeconomic personas in a simulated gambling environment. The study found that GPT-4.1 reproduces key behavioral signatures predicted by Kahneman and Tversky's Prospect Theory without explicit instruction. Poor personas took more risks and played longer sessions, while rich personas were risk-averse. However, the model showed belief rigidity, weak internal belief updating, and generated emotional labels as post-hoc annotations rather than decision drivers. These findings have significant implications for LLM agent design, interpretability, and understanding implicit cognitive biases in pretrained models.
37.4x longer
Session Length (Poor vs. Rich)
4.15 Cohen's d
Risk Score (Poor vs. Rich)
0.39
Emotion-Strategy Consistency (Cramer's V)
0.032
Belief Updating (Poor Persona ρ)
Deep Analysis & Enterprise Applications
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4.15
Cohen's d for Risk Score (Poor vs. Rich)
GPT-4.1 successfully reproduced key behavioral signatures of Prospect Theory across socioeconomic personas. Poor personas played significantly longer (37.4 rounds vs. 1.1 for Rich), exhibited higher risk scores (Cohen's d = 4.15), and took larger proportional bets, aligning with risk-seeking in the loss domain. Rich personas, from a position of financial security, were highly risk-averse.
LLM Decision-Making Process (Observed)
Persona Context & Goal
→
Initial Risk Assessment
→
Sequential Decision (Play/Stop, Bet Size)
→
Observe Outcome
→
Post-hoc Emotional Labeling
| Feature | LLM Behavior | Human Behavior (Prospect Theory) |
|---|---|---|
| Risk Aversion (Gain Domain) |
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| Risk Seeking (Loss Domain) |
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| Emotional Labels |
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| Belief Updating |
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The model exhibited belief rigidity; its risk perception, once set by the persona context, did not meaningfully update across 50 rounds of feedback, even in environments with clear negative outcomes (e.g., Biased Low machine). This suggests LLM agents may require explicit reflection mechanisms for true adaptation.
Mitigating Belief Rigidity in LLM Agents
Challenge: GPT-4.1's risk perception remained stable, suggesting a lack of in-context belief updating despite sequential feedback.
Solution: Implement explicit 'Reflection Frameworks' or 'Belief State Tracking' beyond the base model's implicit learning. This involves structured prompts that force the agent to analyze outcomes, revise its internal estimates, and adapt its strategy.
Outcome: Improved agent adaptability in dynamic, uncertain environments, leading to more robust and reliable decision-making in real-world deployments (e.g., financial trading, medical diagnostics, logistical planning).
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